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COMPUTER SCIENCE DEPARTMENT
Technion - Israel Institute of Technology
July 8, 2012
Industrial Project (234313)
Tube Lifetime Predictive
Algorithm
Students: Nidal Hurani, Ghassan Ibrahim
Supervisor: Shai Rozenrauch
Goals
 Finding tube lifetime predictive algorithm based on
parameters and results of the CT Radar system
 The algorithm target is to predict with a precision of 75%
the lifetime of the tubes
 Algorithm implementation
Obstacles
 Raw data was not reliable
 Completing the missing data in order to use it correctly
 Finding parameters and measures which influence the most
of the lifetime of the tube
 Fit to a known statistical model which can describe the tube
lifetime given these parameters
 Dealing with huge data
Methodology
 Run queries over the database (SQL) to retrieve the relevant
data set
 Processing and transforming the data into a training set
which is used later in the predictive algorithm
 Building a windows form application which can “talk “ with R
Fitting a decision tree using CART ( Classification and Regression
Tree) for the giving training set
 Predict a tube lifetime given a vector of estimated parameters or
measures

Environments &Technologies
 Main programming language - C#
 IDE - Visual studio 2010
 Statistical tool JMP 7 - for finding possible statistical models
which can describe the problem
 EXCEL (MS office)
 R (Statistical Language)
 RCOM
 MSSQL
 JMP 7
Achievements
 A predictor with ±120 days error in general
 76.8293% of the predictions with ±60 days error
 User friendly program
Conclusions
 The more the training set reflect the tube real behavior the
more accurate the algorithm shall predict
 Depends for example on the way of completing the data & also the
amount of data needs to be complete
 Having a comprehensive training set gives more accurate
results
 The algorithm somehow is “flexible”
 Whenever a new parameter is recognized as a huge influencer